Dear MRI Insider,
The annual congress of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) ended on Saturday, and we've posted three articles from this important meeting:
The latest thinking on resting state functional MRI (rsfMRI) came under scrutiny during Friday's hot topic debate. Although rsfMRI shows great potential, opinion is divided about whether these examinations will ever be useful in clinical practice. Get the story here.
Research into rsfMRI also accounted for two of the top prizes given to electronic poster exibits during the ESMRMB congress. The prize-winning studies were conducted in London and Grenoble, France. Click here to read more.
Population imaging, which is epidemiological research done through large-scale imaging in unselected and nondiseased population cohorts, is an emerging concept. Dr. Gabriel Krestin, PhD, immediate past president of the European Society of Radiology, gave a keynote lecture on this topic. To find out more, click here.
MRI is a key modality when it comes to investigating acute abdominal and pelvic pain during pregnancy. The European Society of Urogenital Radiology has updated its guidelines on this topic, and make sure you don't miss them. For the details, click here.
The Journées Françaises de Radiologie Diagnostique et Interventionnelle (JFR, French Congress of Diagnostic and Interventional Radiology) begins next week in Paris, and attention is likely to focus on France's continuing shortage of MRI systems. This is contributing to social inequalities, particularly in cancer imaging, which is the major theme of the conference. For our JFR preview article, click here.
This is a small selection of the articles posted in the MRI Digital Community. Please do check out the rest of them below this message.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
